scholarly journals Biomarkers of Length of Stay on an Inpatient Eating Disorder Unit

2020 ◽  
Author(s):  
Courtney E. Breiner ◽  
Baiyu Qi ◽  
Laura M. Thornton ◽  
Kimberly A. Brownley ◽  
Tonya Foreman ◽  
...  
Keyword(s):  
Alkaline Phosphatase ◽  
Eating Disorder ◽  
Length Of Stay ◽  
Regression Models ◽  
Predictive Ability ◽  
Inpatient Stay ◽  
Treatment Needs ◽  
Duration Of Illness ◽  
Comprehensive Metabolic Panel ◽  
Potential Biomarkers

AbstractLength of stay on an inpatient unit for treatment of anorexia nervosa (AN) is widely variable. Although previous research has used anthropometric and clinical variables and duration of illness to predict length of stay, there has been limited investigation of the predictive ability of biomarkers. Biomarkers, including those collected through a comprehensive metabolic panel (CMP) and appetite hormones, such as ghrelin and leptin, are impacted by disease presence and may play an etiological role in AN. Using a series of regression models, we evaluated the associations of these putative biomarkers with length of inpatient stay in 46 females receiving treatment on an inpatient eating disorder unit. Active ghrelin levels at inpatient admission positively predicted length of stay and alkaline phosphatase at discharge was significantly positively correlated with length of stay. This research provides further evidence supporting both biological and psychological components of AN, identifying potential biomarkers that could aid in prospective prediction of treatment needs. Further research is necessary to replicate and extend these findings across treatment settings.

scholarly journals Biomarkers of Length of Stay On An Inpatient Eating Disorder Unit For Treatment of Anorexia Nervosa in Females

2022 ◽  
Author(s):  
Courtney E. Breiner ◽  
Baiyu Qi ◽  
Laura M. Thornton ◽  
Kimberly A. Brownley ◽  
Tonya Foreman ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Eating Disorder ◽  
Length Of Stay ◽  
Regression Models ◽  
Predictive Ability ◽  
Inpatient Setting ◽  
Inpatient Stay ◽  
Treatment Needs ◽  
Duration Of Illness ◽  
Physical Recovery

Abstract Background. Length of stay on an inpatient unit for treatment of anorexia nervosa (AN) is widely variable. Although previous research has used anthropometric and clinical variables and duration of illness to predict length of stay, there has been limited investigation of the predictive ability of biomarkers. Biomarkers, including those collected through a comprehensive metabolic panel (CMP) and appetite hormones, such as ghrelin and leptin, are impacted by disease presence and may play an etiological role in AN. Methods. Using a series of regression models, we retrospectively evaluated the associations of these putative biomarkers at admission with length of inpatient stay in 59 females receiving treatment on an inpatient eating disorder unit for anorexia nervosa. Results. Both lower levels of magnesium and higher active ghrelin levels at inpatient admission predicted length of stay. Conclusions. This research provides further evidence supporting both biological and psychological components of AN, identifying potential biomarkers that could aid in prospective prediction of treatment needs. Ghrelin monitoring throughout inpatient stays may aid clinicians in better predicting physical recovery and renourishment from AN and prepare for stepdown from an inpatient setting. Further research is necessary to replicate and extend these findings across treatment settings.

scholarly journals Healthcare Utilization Due to Suicide Attempts among Homeless Youth in New York State

2021 ◽  
Author(s):  
Rie Sakai-Bizmark ◽  
Hiraku Kumamaru ◽  
Dennys Estevez ◽  
Emily H Marr ◽  
Edith Haghnazarian ◽  
...  
Keyword(s):  
Emergency Department ◽  
New York ◽  
Length Of Stay ◽  
Suicide Attempt ◽  
Healthcare Utilization ◽  
Regression Models ◽  
Homeless Youth ◽  
Suicide Attempts ◽  
New York State ◽  
Individual Characteristics

Abstract Suicide remains the leading cause of death among homeless youth. We assessed differences in healthcare utilization between homeless and non-homeless youth presenting to the emergency department or hospital after a suicide attempt. New York Statewide Inpatient and Emergency Department Databases (2009–2014) were used to identify homeless and non-homeless youth ages 10 to 17 who utilized healthcare services following a suicide attempt. To evaluate associations with homelessness, we used logistic regression models for mortality, use of violent means, intensive care unit utilization, log-transformed linear regression models for hospitalization cost, and negative binomial regression models for length of stay. All models were adjusted by individual characteristics with a hospital random effect and year fixed effect. We identified 18,026 suicide attempts with healthcare utilization rates of 347.2 (95% Confidence Interval [CI]: 317.5, 377.0) and 67.3 (95%CI: 66.3, 68.3) per 100,000 person-years for homeless and non-homeless youth, respectively. Length of stay for homeless youth was statistically longer than non-homeless youth (Incidence Rate Ratio 1.53; 95%CI: 1.32, 1.77). All homeless youth who visited the emergency department after a suicide attempt were subsequently hospitalized. This could suggest a higher acuity upon presentation among homeless youth compared with non-homeless youth. Interventions tailored to homeless youth should be developed.

45 Application of Machine Learning Models to Thermal Burn Patient Outcome Predictions in the Aftermath of a Nuclear Event

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S33-S34
Author(s):  
Morgan A Taylor ◽  
Randy D Kearns ◽  
Jeffrey E Carter ◽  
Mark H Ebell ◽  
Curt A Harris
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Large Scale ◽  
Prediction Models ◽  
Burn Patients ◽  
Thermal Burn ◽  
Logistic Regression Models ◽  
Burn Patient

Abstract Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.

Towards reliable estimation of an “electronic tongue” predictive ability from PLS regression models in wine analysis

Talanta ◽  
2012 ◽  
Vol 90 ◽  
pp. 109-116 ◽  
Author(s):  
Dmitry Kirsanov ◽  
Olga Mednova ◽  
Vladimir Vietoris ◽  
Paul A. Kilmartin ◽  
Andrey Legin
Keyword(s):  
Regression Models ◽  
Electronic Tongue ◽  
Predictive Ability ◽  
Pls Regression ◽  
Reliable Estimation ◽  
Wine Analysis

scholarly journals Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Epaminondas Markos Valsamis ◽  
David Ricketts ◽  
Henry Husband ◽  
Benedict Aristotle Rogers
Keyword(s):  
Time Series ◽  
Hip Fracture ◽  
Linear Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Retrospective Studies ◽  
Linear Regression Models ◽  
Time To Surgery ◽  
Before And After ◽  
Group Comparisons

Introduction. In retrospective studies, the effect of a given intervention is usually evaluated by using statistical tests to compare data from before and after the intervention. A problem with this approach is that the presence of underlying trends can lead to incorrect conclusions. This study aimed to develop a rigorous mathematical method to analyse temporal variation and overcome these limitations. Methods. We evaluated hip fracture outcomes (time to surgery, length of stay, and mortality) from a total of 2777 patients between April 2011 and September 2016, before and after the introduction of a dedicated hip fracture unit (HFU). We developed a novel modelling method that fits progressively more complex linear sections to the time series using least squares regression. The method was used to model the periods before implementation, after implementation, and of the whole study period, comparing goodness of fit using F-tests. Results. The proposed method offered reliable descriptions of the temporal evolution of the time series and augmented conclusions that were reached by mere group comparisons. Reductions in time to surgery, length of stay, and mortality rates that group comparisons would have credited to the hip fracture unit appeared to be due to unrelated underlying trends. Conclusion. Temporal analysis using segmented linear regression models can reveal secular trends and is a valuable tool to evaluate interventions in retrospective studies.

Markov Breaks in Regression Models

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Aaron Smith
Keyword(s):  
Regression Models ◽  
Yield Curve ◽  
Likelihood Function ◽  
Weighted Average ◽  
Predictive Ability ◽  
Error Variance ◽  
Monte Carlo Integration ◽  
Linear Regression Models ◽  
Out Of Sample ◽  
Low Dimensional

This article develops a new Markov breaks (MB) model for forecasting and making inference in linear regression models with breaks that are stochastic in both timing and magnitude. The MB model permits an arbitrarily large number of abrupt breaks in the regression coefficients and error variance, but it maintains a low-dimensional state space, and therefore it is computationally straightforward. In particular, the likelihood function can be computed analytically using a single iterative pass through the data and thereby avoids Monte Carlo integration. The model generates forecasts and conditional coefficient predictions using a probability weighted average over regressions that include progressively more historical data. I employ the MB model to study the predictive ability of the yield curve for quarterly GDP growth. I show evidence of breaks in the predictive relationship, and the MB model outperforms competing breaks models in an out-of-sample forecasting experiment.

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